AI implementation in AML processes in just five steps
published : 6 Dec 2021 at 08:00
New money laundering techniques force companies to ramp up security measures. But, how to keep up while meeting tight regulations and reducing costs and workload? Just add one additional module to your existing processes – an AI module.
Our infographic below shows a simplified view on the challenges that you might face. While some phases of the AI module project are straightforward, others, like data training and testing, go back and forth, and you will need to spend extra time on them.
Artificial Intelligence implementation – a simplified graph
1. Start off strong
The more meticulous and methodological you are in the beginning, the more time you will save during the later stages.
Firstly, look internally and identify the problem. You might notice that your AML process is inefficient because of the large volumes of false hits. Remember to thoroughly define your problem and evaluate your sources of data. Ask yourself, how much data and which in particular you actually need. Then, select the right vendor. Relying on tested external solution providers will probably be the best choice.
After that, you can verify your data. Is it easily retrievable, complete, interpretable? Only after figuring this out can you set your KPI targets, and in so doing, manage your expectations. The last step here is to select your deployment type. Talk options with your IT department and the vendor – both parties need to agree on one thing.
2. Set up the conditions
There are only three steps here, and the first one is to review sharing policies. Bear in mind that, in order to learn, AI can handle only a limited amount of restrictions. After having worked out the right degree, go on and perform data anonymization. This will replace all sensitive data, like clients’ surname, or ID card, in order to prevent any data breaches.
Finally, you can share data, for which you have to obey a highly regulated protocol. Both you and the vendor must meet all transfer criteria, and the compliance/security department should scrutinize the process.
3. Begin AI modelling
Here is where your vendor takes over. Before starting to work on the AI model, data needs to be prepared first. After all, not every piece of data will be exposed; some percentage (usually 20%) must be put aside for later testing.
Your vendor will now conduct an exploratory data analysis, which will let you know if some data is missing, incomplete, or cannot be precisely interpreted. For better results, you should tightly cooperate with the vendor. Next, make room for feature engineering; here, data scientists will transform data (and add special features to it) for the AI to make better predictions. During the iterative process of AI learning, spare some time to optimize. Select a certain model, train, and test – only then will you be able to choose the best one.
4. Make the final evaluation
Now is the time to test the AI model. Use the remaining 20% of data for full data ingestion and shared dataset testing. Don’t forget about double blind testing. Here, the vendor doesn’t know which cases in the dataset are money laundering. If the AI model can detect it, you can be sure of its accuracy.
Next, make a report summary. Measure the results, create a report, and compare the outcomes with expectations. Lastly, leave yourself some space for refining, adjusting, and enhancing.
Once you are sure that your AI model works, integrate it with your existing solution. If it is absolutely flawless and complete, you can go live. Launch your AI model; the solution needs to be incorporated into the existing processes. Once the AI is live, monitor the results. As time goes by and money laundering techniques evolve, you need to ensure the model is up-to-date, so re-evaluate it periodically.
Consult industry experts and expand your knowledge on the subject. Implementing AI is a complex process, but, if used properly, this technology can greatly enhance the capabilities of a company.
See the results of a project like this here